In the field of medical image computing, image registration is the process of generating image alignment data in order to enable the correct alignment of medical images. In particular, image registration is the process of transforming sets of data into one coordinate system. Such image data may comprise photographs, data from different sensors, from different viewpoints and/or at different times. Medical image computing typically operates on uniformly sampled data with regular x-y-z spatial spacing (images in 2D and volumes in 3-D, generically referred to as images). Image registration is necessary in order to be able to compare or integrate the different sets of data.
Medical images obtained through a variety of modalities, such as CT (computed tomography), PET (positron emission tomography), and MRI (magnetic resonance imaging), are commonly acquired in groups with little patient motion between acquisitions. For example, MRI images are often acquired using multiple pulse sequences to generate different image appearance, gated image sequences where images acquired for different points of, say, the breathing or cardiac cycle, dynamic sequences where the uptake of an image contrast agent is observed using multiple images, etc.
Images may require alignment with each other for a variety of reasons, for example with the use of multiple imaging modalities for radiotherapy contouring or assessment of follow-up images taken over a number of years. Images obtained using different modalities or at different times may have substantial differences between patient position, or other differences such as changes in anatomy due to growth etc. For example, a pair of PET/CT images, which are in the same frame of reference because they were acquired on a hybrid scanner, may require alignment to a series of MRI images of different pulse sequences. The MRI images would typically comprise a different frame of reference to the PET/CT images, but would be approximately aligned to each other as a group acquired at a similar time.
Rigid, affine and deformable image registration methods may be used to correct for differences within images to be aligned to different extents. In rigid alignment, translation and rotation of the images may be performed. In affine alignment, shearing and scaling of the images may be performed in addition to translation and rotation. In deformable alignment, translation of individual points within an image is able to be performed.
Various methods for aligning a pair of images are known, such as “Medical image registration”; D Hill, P Batchelor, M Holden and D Hawkes. Phys Med Biol 2001;46:R1-R45, and “A viscous fluid model for multimodal non-rigid image registration using mutual information”; E D′Agostino, F. Maes, D. Vandermaeulen and P. Suetens. Medical Image Analysis 2003; 7(4):565-575.
The simplest application is to register a single image to another single image. However, in a clinical situation, there may be numerous images for a patient that a clinician would like to be registered with one another. Such images may be obtained via different ‘acquisitions’, each acquisition potentially comprising multiple individual images. The different acquisitions may relate to different imaging modalities used to obtain the respective images, different acquisition times for the respective images, etc.
One approach is to simply register every image with every other image. However, this approach can be prohibitively time consuming when there are a relatively large number of images to be so registered. In addition, such direct registration may also lead to poor registrations in some instances where there is weak information content in some of the images. Inconsistencies between registrations may also arise whereby, for example, despite all images in an acquisition being approximately the same, the registrations back to an image in a different acquisition end up being substantially different.
Another known approach is to register images within a group to a common frame of reference. In this approach all images are registered to either a single image or to an average image constructed from all the images in the group. This is known as group-wise registration whereby registration between any pair of images can be calculated by performing the forward transformation to the reference space, followed by the inverse transformation to the target image. Group-wise registration is discussed in greater detail in “A unified information-theoretic approach to groupwise non-rigid registration and model building”; C. Twinning, T Cootes, S Marsland, V. Petrovic, R. Schestowitz and C. Taylor. Lecture Notes in Computer Science 2005;3565:167-198.
However, group-wise registration suffers from the drawback that the registrations performed may be unsuitable, and that the information content between the images may be weak, leading to numerically unstable registration. For example, the information content, i.e. the strength of features used for registration, of a PET image from one acquisition may be low, so registration of this via a reference image, for example a CT image of a different acquisition to a CT image of the same acquisition as the PET image may be less suitable than direct registration.
Thus, there is a need for a computationally efficient method and apparatus for performing reliable registration of medical images.